segmentation of volumetric images for accurate distinction of biologically significant entities
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Segmentation of volumetric images for accurate distinction of biologically significant entities. Ryan Green. Contents. Problems with volumetric images Current approaches to image analysis Flood-Fill Parallelised RAM-efficient Flood-Fill Conclusion. Problems with volumetric images. - PowerPoint PPT PresentationTRANSCRIPT
Segmentation of volumetric images for accurate distinction of
biologically significant entities
Ryan Green
Contents
• Problems with volumetric images• Current approaches to image analysis• Flood-Fill• Parallelised RAM-efficient Flood-Fill• Conclusion
Problems with volumetric images
• Difficult to determine biologically significant entities in 2D
• Difficult to visualise in 3D
• Large processor and RAM requirements
Magnetic Resonance Images
Standard MR Images in 2 and 3 Dimensions
How images are currently analysed• Manual 2D visual analysis• Manual 3D visual analysis via transparencies
according to tissue intensity• Automated/Semi-Automated Image segmentation via
Fuzzy C-Means (FCM) and extensions to FCM
FCM segmentation in to Grey Matter (GM), White Matter (WM) and Cerebrospinal Fluid (CSF)
Standard Flood-fillFlood-fill (node, target-color, replacement-color): 1. If the color of node is not equal to target-color, return. 2. Set the color of node to replacement-color. 3. Perform Flood-fill (one step to the west of node, target-color, replacement-color). Perform Flood-fill (one step to the east of node, target-color, replacement-color). Perform Flood-fill (one step to the north of node, target-color, replacement-color). Perform Flood-fill (one step to the south of node, target-color, replacement-color). 4. Return.
• Theoretically sound• Realistically breaks down in a
stack overflow
___________________________________________________
Improved Flood-Fill
Flood-fill (node, target-color, replacement-color): 1. Set Q to the empty queue. 2. If the color of node is not equal to target-color, return. 3. Add node to the end of Q. 4. While Q is not empty: 5. Set n equal to the first element of Q 6. If the color of n is equal to target-color, set the color of n to replacement-color. 7. Remove first element from Q 8. If the color of the node to the west of n is target-color: 9. Add that node to the end of Q10. If the color of the node to the east of n is target-color: 11. Add that node to the end of Q12. If the color of the node to the north of n is target-color:13. Add that node to the end of Q14. If the color of the node to the south of n is target-color:15. Add that node to the end of Q16. Return.
Real-World implementations
• Many optimisations are possible such as:o East-West loopso Scanline Fillso Boundary condition checks
• Yet most still conform to the same logical basis as the recursive algorithm
User driven centroid selection
• Used for isolation of a specific pre-defined biologicially significant entity via a modified parallel flood-fill algorithm
3 images from a 4000x4000x4000 volumetric image, at depths at the end of the first, second, and third quarters
Parallelised RAM-Efficient 3D Flood-Fill• Improve Queue based Flood-Fill with East-West
optimisation• Each slice of the volumetric image on the z-axis
possesses it's own queue• The volumetric image is divided along the z-axis
according to the number of processes available• Boundary slices (the single slices between divided
segments) are left until last to prevent clashes• Only slices currently being used by a process are
loaded into RAM
Possible example output
Images from Google Body showing differing layers of anatomy of a synthetic person
Conclusion
• There is a problem faced by many researchers dealing with large volumetric images
• Current processes are insufficient to handle the increasing number and complexity of images
• Parallelised 3D flood-fill with user defined centroids will assist researchers in accurately separating biologically significant entities of interest for isolated analysis